| IHME | WHO | Dif | % difference (WHO / IHME) | % difference (IHME / WHO) | ||
|---|---|---|---|---|---|---|
| Total | HIV+TB only | 211604 | 389042 | 177438.13321 | 83.85% | -45.61% |
| TB only | 1111312 | 1379440 | 268128.44955 | 24.13% | -19.44% | |
| Total TB | 1322916 | 1768482 | 445566.58275 | 33.68% | -25.19% | |
| Adults | HIV+TB only | 177567 | 348026 | 170458.90473 | 96% | -48.98% |
| TB only | 1075691 | 1210620 | 134929.12946 | 12.54% | -11.15% | |
| Total TB | 1253257 | 1558645 | 305388.03419 | 24.37% | -19.59% | |
| Children | HIV+TB only | 34037 | 41016 | 6979.22848 | 20.5% | -17.02% |
| TB only | 35621 | 168821 | 133199.32009 | 373.93% | -78.9% | |
| Total TB | 69659 | 209837 | 140178.54857 | 201.24% | -66.8% | |
| Female | HIV+TB only | 78110 | 143496 | 65386.51804 | 83.71% | -45.57% |
| TB only | 367764 | 352488 | 15276.43876 | -4.15% | 4.33% | |
| Total TB | 445874 | 495984 | 50110.07929 | 11.24% | -10.1% | |
| Male | HIV+TB only | 99457 | 204471 | 105013.90757 | 105.59% | -51.36% |
| TB only | 707927 | 858132 | 150205.56821 | 21.22% | -17.5% | |
| Total TB | 807383 | 1062603 | 255219.47578 | 31.61% | -24.02% | |
| AMR | HIV+TB only | 579 | 620 | 41.31917 | 7.14% | -6.66% |
| TB only | 2036 | 1914 | 122.37010 | -6.01% | 6.39% | |
| Total TB | 2615 | 2534 | 81.05093 | -3.1% | 3.2% | |
| EMR | HIV+TB only | 165 | 533 | 368.30203 | 223.05% | -69.04% |
| TB only | 14658 | 14572 | 85.51575 | -0.58% | 0.59% | |
| Total TB | 14823 | 15106 | 282.78629 | 1.91% | -1.87% | |
| EUR | HIV+TB only | 212 | 374 | 161.68749 | 76.15% | -43.23% |
| TB only | 2383 | 2999 | 615.93832 | 25.85% | -20.54% | |
| Total TB | 2595 | 3373 | 777.62581 | 29.97% | -23.06% | |
| SEA | HIV+TB only | 19310 | 28870 | 9560.04060 | 49.51% | -33.11% |
| TB only | 333250 | 345889 | 12639.43214 | 3.79% | -3.65% | |
| Total TB | 352560 | 374759 | 22199.47275 | 6.3% | -5.92% | |
| WPR | HIV+TB only | 2057 | 2010 | 47.13348 | -2.29% | 2.35% |
| TB only | 39055 | 28351 | 10704.20283 | -27.41% | 37.76% | |
| Total TB | 41112 | 30361 | 10751.33632 | -26.15% | 35.41% |
Table with model output for estimating likelihood or magnitude of difference in estimates by HIV, age, sex, and region.
This section is unfinished.
Rankings of highest absolute and standardized differences for IHME and WHO.
Rankings of highest absolute and standardized differences for IHME and WHO.
The below scatterplot shows the correlation between WHO (x-axis) estimates and IHME (y-axis) estimates, with each point colored by its (WHO-defined) region.
In the following four charts, Libya has been excluded as an outlier.
Linear regression to estimate effect of prevalence survey on absolute difference in cases (WHO minus IHME), adjusting for region.
95% confidence intervals
Linear regression to estimate effect of prevalence survey on adjusted standardized difference in cases, adjusting for region.
95% confidence intervals
(Unfinished)
Correlation of adjusted stand diff with a) HIV prevalence, CDR by both, CFR, MDR prevalence.
cor(df$adjusted_stand_dif, df$newrel_hivpos, use = 'complete.obs')
[1] 0.03007348
cor(df$adjusted_stand_dif, df$gb_c_cdr, use = 'complete.obs')
[1] -0.3206714
cor(df$adjusted_stand_dif, df$cdr_ihme, use = 'complete.obs')
[1] 0.061843
cor(df$adjusted_stand_dif, df$case_fatality_rate_2014, use = 'complete.obs')
[1] -0.1870433
cor(df$adjusted_stand_dif, df$case_fatality_rate_2012_to_2014, use = 'complete.obs')
[1] -0.1835658
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015, use = 'complete.obs')
[1] -0.1951491
cor(df$adjusted_stand_dif, df$case_fatality_rate_2014_new, use = 'complete.obs')
[1] -0.1849203
cor(df$adjusted_stand_dif, df$case_fatality_rate_2012_to_2014_new, use = 'complete.obs')
[1] -0.1775794
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015_new, use = 'complete.obs')
[1] -0.1891621
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015_adjusted, use = 'complete.obs')
[1] -0.1935328
cor(df$adjusted_stand_dif, df$p_mdr_new, use = 'complete.obs')
[1] -0.0182285
cor(df$adjusted_stand_dif, df$reported_mdr, use = 'complete.obs')
[1] -0.05113626
Does region affect likelihood of having a prevalence survey?
xt <- table(df$prevsurvey, df$who_region)
xt
AFR AMR EMR EUR SEA WPR
0 37 37 20 52 8 22
1 10 0 2 0 3 4
chisq.test(xt)
Pearson's Chi-squared test
data: xt
X-squared = 21.511, df = 5, p-value = 0.0006482
Does having a prev survey affect the adjusted stand diff?
t.test(x = df$adjusted_stand_dif[df$prevsurvey == 0],
y = df$adjusted_stand_dif[df$prevsurvey == 1])
Welch Two Sample t-test
data: df$adjusted_stand_dif[df$prevsurvey == 0] and df$adjusted_stand_dif[df$prevsurvey == 1]
t = -1.1965, df = 22.444, p-value = 0.244
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-7.202375 1.928552
sample estimates:
mean of x mean of y
0.8763825 3.5132939